Cystic Fibrosis-related Diabetes Clinical Trial
Official title:
EnVision CF Multicenter Study of Glucose Tolerance in Cystic Fibrosis
NCT number | NCT03650712 |
Other study ID # | 201809715 |
Secondary ID | |
Status | Completed |
Phase | N/A |
First received | |
Last updated | |
Start date | July 1, 2019 |
Est. completion date | August 31, 2023 |
Verified date | November 2023 |
Source | University of Iowa |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
Cystic Fibrosis Related Diabetes has been identified by the CF community as one of the top ten priorities for CF research. In CF clinical decline due to dysglycemia begins early, prior to diagnosis of diabetes and increases mortality from pulmonary disease. There is presently no way to determine who, of those with dysglycemia, will experience clinical compromise. However, the CF Center in Milan has found that measurable age- and sex-dependent variables on oral glucose tolerance testing (OGTT) predict β-cell failure-the primary driver of decline in CF. the investigators propose a multi-center trial to develop nomograms of age and sex dependent reference values for OGTT-derived measures including glucose, insulin, c-peptide, and the resultant OGTT-derived estimates of β-cell function, β cell sensitivity to glucose, and oral glucose insulin sensitivity (OGIS) and to determine correlation of these with clinical status (FEV-1, BMI z score, number of pulmonary exacerbations over the past 12 months). In a subset of the cohort the investigators will perform additional studies to determine possible mechanisms driving abnormal β cell function, including the role of lean body mass (as measured by DXA), impact of incretin (GLP-1, GIP) and islet hormones (glucagon, pancreatic polypeptide) on β cell function and the relationship of reactive hypoglycemia and catecholamine responses to β cell function, as well as the relationship of β cell sensitivity to glucose as determined by our model to abnormalities in blood glucose found in a period of free living after the study (determined by continuous glucose monitoring measures (Peak glucose, time spent >200 mg/dl, standard deviation). the investigators will also develop a biobank of stored samples to allow expansion to the full cohort if warranted and to enable future studies of dysglycemia and diabetes in CF. the investigator's eventual goal is utilization of the nomograms to determine the minimum number of measures to accurately predict risk for clinical decline from dysglycemia in CF.
Status | Completed |
Enrollment | 317 |
Est. completion date | August 31, 2023 |
Est. primary completion date | August 31, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 6 Years and older |
Eligibility | Inclusion Criteria: 1. Age >/= 6 years 2. Diagnosis of cystic fibrosis 3. CF patients regularly attending the CF centers 4. Clinically stable in previous 3wks: - absence of major clinical events including pulmonary exacerbations, - no change in their habitual treatment regimen including introduction of antibiotics or steroids in the past 3 weeks Exclusion Criteria: 1. Diagnosis of type 1 diabetes, type 2 diabetes, or MODY 2. Organ transplantation 3. new diagnosis of CFRD in the past 6 months 4. antidiabetic treatment in past 6 mos (insulin or oral hypoglycemic agents) -patients with previous CFRD diagnosis, but not currently taking insulin/glucose-lowering medications for at least 6 months should be included 5. pulmonary exacerbation associated with systemic steroid requirement in the last 6 months 6. on CFTR corrector less than 6 months prior to enrollment |
Country | Name | City | State |
---|---|---|---|
United States | University of Colorado | Aurora | Colorado |
United States | University of Iowa | Iowa City | Iowa |
United States | University of Minnesota | Minneapolis | Minnesota |
United States | Washington University St. Louis | Saint Louis | Missouri |
Lead Sponsor | Collaborator |
---|---|
Katie Larson Ode |
United States,
Battezzati A, Bedogni G, Zazzeron L, Mari A, Battezzati PM, Alicandro G, Bertoli S, Colombo C. Age- and Sex-Dependent Distribution of OGTT-Related Variables in a Population of Cystic Fibrosis Patients. J Clin Endocrinol Metab. 2015 Aug;100(8):2963-71. doi: 10.1210/jc.2015-1512. Epub 2015 Jun 9. — View Citation
Mari A, Ferrannini E. Beta-cell function assessment from modelling of oral tests: an effective approach. Diabetes Obes Metab. 2008 Nov;10 Suppl 4:77-87. doi: 10.1111/j.1463-1326.2008.00946.x. — View Citation
Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ. A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care. 2001 Mar;24(3):539-48. doi: 10.2337/diacare.24.3.539. Erratum In: Diabetes Care. 2014 Jul;37(7):2063. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Other | the correlation between fat mass and fat free mass as determined by DXA and beta cell function as measured by beta cell sensitivity to glucose | We will measure the correlation between fat mass and fat free mass as determined by DXA and beta cell function as measured by beta cell sensitivity to glucose. The model will be adjusted for BMI z-score, weight Z score, sex, age, and genotype classification, as well as therapy with CFTR potentiator/correctors. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for GLP-1 is a significant predictor of beta cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate model if GLP-1 is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for GIP is a significant predictor of beta cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate model if GIP is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for PP is a significant predictor of beta cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate model if PP is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for glucagon is a significant predictor of beta cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate model if glucagon is a significant predictor of beta cell glucose sensitivity, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for GLP-1 is a significant predictor of OGIS | linear regressions will be used to model the responses We will then determine, in a multivariate model if GLP-1 is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for GIP is a significant predictor of OGIS | linear regressions will be used to model the responses We will then determine, in a multivariate model if GIP is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for PP is a significant predictor of OGIS | linear regressions will be used to model the responses We will then determine, in a multivariate model if PP is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Determine whether the area under the curve (AUC) for glucagon is a significant predictor of OGIS | linear regressions will be used to model the responses We will then determine, in a multivariate model if glucagon is a significant predictor of OGIS, also controlling for potential confounders such as age, sex, genotype classification and therapy with CFTR potentiator/correctors. | data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | mean epinephrine levels in subject with symptomatic hypoglycemia versus those without hypoglycemia | Mean catecholamine levels (epinephrine, norepinephrine) at blood glucose nadir will be compared between subjects who do and do not report symptomatic hypoglycemia. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | mean norepinephrine levels in subject with symptomatic hypoglycemia versus those without hypoglycemia | Mean catecholamine levels (epinephrine, norepinephrine) at blood glucose nadir will be compared between subjects who do and do not report symptomatic hypoglycemia. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Correlation between CGM Time spent >140 mg/dl, and b-cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate model Correlation between CGM Time spent >140 mg/dl, and b-cell glucose sensitivity will be determined, controlling for potential confounders | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Correlation between CGM Time spent >200 mg/dl, and b-cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate model Correlation between CGM Time spent >200 mg/dl, and b-cell glucose sensitivity will be determined, controlling for potential confounders. | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Correlation between CGM peak glucose and b-cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate modelCorrelation between CGM peak glucose and b-cell glucose sensitivity will be determined, controlling for potential confounders | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Other | Correlation between CGM standard deviation of sensor glucose values and b-cell glucose sensitivity | linear regressions will be used to model the responses We will then determine, in a multivariate modelCorrelation between CGM standard deviation of sensor glucose values and b-cell glucose sensitivity will be determined, controlling for potential confounders | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Primary | age- and sex-based nomograms for beta cell glucose sensitivity | The primary endpoint is relationship of beta cell sensitivity to glucose to age. To assess the primary endpoint, the following method will be used: Quantiles of beta-cell glucose sensitivity will be calculated using quantile regression. The outcome variable of the quantile curve is beta-cell glucose sensitivity (continuous, picomol per minute-1 per meter-2 per millimole-1) and the predictor variable is age (continuous, years). On the basis of the available data, we expect a linear relationship between beta-cell glucose sensitivity and age with no heteroskedasticy | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Primary | age- and sex-based nomograms for OGIS (oral glucose insulin sensitivity) | The primary outcome of this study is to establish age- and sex-based nomograms ("growth charts") ranging from the 5th-95th % for OGIS (oral glucose insulin sensitivity) | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and BMI Z-score | The relationship of the nomogram for beta cell glucose sensitivity will be assessed by quantile regression with the outcome variable beta cell glucose sensitivity, and the predictor the outcomes BMI Z score | data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and FEV-1 | The relationship of the nomogram for beta cell glucose sensitivity will be assessed by quantile regression with the outcome variable beta cell glucose sensitivity, and the predictor the outcomes FEV1 score | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for beta cell glucose sensitivity and pulmonary exacerbations in the previous 12 months | The relationship of the nomogram for beta cell glucose sensitivity will be assessed by quantile regression with the outcome variable beta cell glucose sensitivity, and the predictor the outcome pulmonary exacerbations in the previous 12 months | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for OGIS and BMI z-score | The relationship of the nomogram for OGIS will be assessed by quantile regression with the outcome variable OGIS, and the predictor the outcomes BMI Z score | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for OGIS and FEV-1 | The relationship of the nomogram for OGIS will be assessed by quantile regression with the outcome variable OGIS, and the predictor the outcomes FEV-1 | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for OGIS and the number of pulmonary exacerbations in the previous 12 months | The relationship of the nomogram for OGIS will be assessed by quantile regression with the outcome variable OGIS, and the predictor the outcome the number of pulmonary exacerbations in the previous 12 months | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for insulin and BMI z-score | The relationship of the nomogram for insulin will be assessed by quantile regression with the outcome variable insulin, and the predictor the outcomes BMI Z score | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for insulin and FEV-1 | The relationship of the nomogram for insulin will be assessed by quantile regression with the outcome variable insulin, and the predictor the outcomes FEV-1 | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for insulin and number of pulmonary exacerbations in the previous 12 months | The relationship of the nomogram for insulin will be assessed by quantile regression with the outcome variable insulin, and the predictor the number of pulmonary exacerbations in the previous 12 months | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for c-peptide and BMI z-score | The relationship of the nomogram for beta c-peptide will be assessed by quantile regression with the outcome variable c-peptide, and the predictor the outcomes BMI Z score | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for c-peptide and FEV-1 | The relationship of the nomogram for beta c-peptide will be assessed by quantile regression with the outcome variable c-peptide, and the predictor the outcomes FEV-1 | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months | |
Secondary | evaluate the relationships between age and sex-based quantiles for c-peptide and number of pulmonary exacerbations in the previous 12 months | The relationship of the nomogram for beta c-peptide will be assessed by the same method (quantile regression) with the outcome variable c-peptide, and the predictor the outcomes number of pulmonary exacerbations in the previous 12 months | the data will be gathered at the study visit (one visit of 4+ per year over the 3 years of the study)-data analysis will occur at study close (approximately 2.5 to 3 years from the start of enrollment) data analysis and modeling will take 1-2 months |
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